Specific robotic solutions are currently being developed to aid farmers in the growth of annual row crops, such as corn. Specific solutions include improvements in tailoring the amount of fertilizer added to a particular area of an agricultural field to fit the needs of the crops within that area, fertilizing crops that have grown to a height where use of conventional fertilization equipment would be impractical, seeding a second cover crop while a first crop is still growing or mature and still on the field and/or the collection of various data to maximize the output of an agricultural field. Several examples of unmanned agricultural robots are disclosed in U.S. Pat. Nos. 9,288,938; 9,392,743; and 9,265,187, the contents of which are incorporated by reference herein.
Unmanned agricultural robots that operate autonomously in agricultural settings require geospatial data as a basis for their operation. In a typical application, a field perimeter defines an absolute boundary across which an unmanned agricultural robot is restricted from crossing for safety reasons. Within the perimeter, various more refined data may be required to facilitate operation. For example, high-resolution data on the actual crop row positions, either previously collected or generated on-the-fly, may be necessary to prevent crop damage by the unmanned agricultural robot.
In cases where the unmanned agricultural robot is expected to navigate between two adjacent crop rows, the geospatial data is often referred to as an “as-planted map,” which is typically created using the GPS-based “precision planting” system on the tractor used for planting operations. However, not all fields are planted with GPS-based systems, and the geospatial data for those fields that are planted with GPS-based systems can be of variable accuracy.
In a typical operation of an unmanned agricultural robot, the geospatial data of the crop location is combined with sensors onboard the unmanned robotic platform for fine-scale navigation. That is, one or more sensors determine the proximity of the unmanned agricultural robot side-to-side between the crop rows, thereby providing feedback to the unmanned agricultural robot's control system, which in turn continually adjusts the orientation of the unmanned agricultural robot relative to the crop rows.
Assuming that there is a high-quality as-planted map for a field, as well as onboard sensors for understanding the precise location of crop rows, there can still be unexpected situations that would impact the navigation of an unmanned agricultural robot, such as mis-planted rows or weeds. Thus, even the best current precision planting technology may not be sufficient for fully enabling operation of the unmanned agricultural robot on agricultural fields.
What is needed for robust navigation of unmanned agricultural robots on agricultural fields is an on-board system that can learn essential details of the field in real time. Such a system would be flexible in the sense that it could map the entire field with minimal pre-existing information, taking into account challenges such as mis-planted rows and patches of weeds.